As the demand for processing power for artificial intelligence (AI) applications grows, semiconductor companies are racing to develop AI-specific silicon. The AI market is incredibly dynamic, with more than 50 startups and 25 established semiconductor companies all racing to capture portions of the emerging segment. This soaring growth in AI companies has created an environment of intense competition.
Success for these companies depends on getting to market quickly, and that means finding design and test solutions that address the challenges of the new AI chip architectures with the goals of achieving quality silicon with the fast time-to-market. We will focus here on the design of AI hardware, specifically, how to best test AI chips.
Both established semiconductor companies and a host of new startups are creating processors to handle the computer requirements specific to AI applications. Companies like Intel, Nvidia, and AMD continue to develop and optimize the existing architectures like GPU, CPU, and FPGA. AI processor startups like Graphcore and Mythic are creating ASICs based on the novel, massively parallel architectures that maximize the data processing capabilities for the AI workloads.